// AI Consulting
AI strategy that survives the roadmap review.
We help leadership teams decide what to build, what to buy, and what to kill — then we stay long enough to ship it. No slide-only engagements.
// Who this is for
Built for teams who are past the experiment phase.
CTOs and VPs of Engineering at Series A–C SaaS scale-ups weighing build vs. buy on AI features.
Mid-market CEO/COOs ($10–100M revenue) who need an opinionated AI roadmap their board will actually fund.
APAC enterprise digital and innovation leads in BFSI, retail, and manufacturing navigating compliance-heavy AI adoption.
// What we deliver
The scope, in plain language.
Every engagement is scoped against your business outcome, not a fixed menu. What you see below is the typical shape — we tighten it with you in the first week.
- Opportunity assessment across your product, operations, and customer workflows — scored by ROI and feasibility.
- A 12-month AI roadmap with staged milestones, budget envelopes, and clear exit ramps for each bet.
- Vendor and model selection: commercial LLM vs. open-weight vs. fine-tuned, with total-cost-of-ownership math.
- Data readiness audit covering quality, lineage, consent, and retention against your target use cases.
- Team topology recommendations — when to hire, when to partner, what a sane in-house AI org looks like for your stage.
- Risk and governance framework aligned to EU AI Act, India DPDP, and sector regulators relevant to your market.
- Executive enablement sessions so your leadership can pressure-test the plan in their own language.
// How we work
The Ankor 7-stage framework, applied to ai consulting.
- 01Discover
Align on business outcome, constraints, and success metric.
- 02Define
Pin down scope, architecture, and the evaluation bar.
- 03Design
Model, data, and UX design — with trade-offs on the table.
- 04Data
Audit, remediate, and pipe the data the build actually needs.
- 05Develop
Ship the system in small, testable increments against the eval bar.
- 06Deploy
Rollout with shadow mode, guardrails, and rollback.
- 07Drive
Operate, measure, and iterate — handoff or retainer.
// Outcomes you can expect
Ranges, not guarantees. Specific, not boastful.
From first workshop to a board-ready roadmap with funded phase-one scope.
Prioritized, validated with data availability, and sequenced against delivery capacity.
Every recommendation ships with a reference architecture and a working prototype path.
// Why Ankor
A decade of shipping software, repointed at production AI.
- 10
- years shipping software
- 190+
- clients delivered
- 260+
- products shipped
- 800K+
- daily users served
Serving clients across APAC, the US, and EMEA.
The engagement in plain terms
Most AI strategy decks age in weeks. We write roadmaps that compile — every recommendation maps to a reference architecture, a staffing plan, and a 90-day proof. When you bring us in, you are buying engineering judgment, not slideware.
We have spent 10 years shipping production software for 190+ clients. The last four years of that work have been overwhelmingly AI — LLM applications, retrieval systems, agent workflows, and the unglamorous data plumbing that makes them actually work in production. That is the lens we bring to your strategy.
What a typical discovery looks like
A 4–6 week discovery covers three tracks in parallel: opportunity mapping with your business leaders, technical and data due diligence with your engineering team, and market/vendor scan on the model and tooling landscape for your use cases. We close with a working-session read-out where your leadership interrogates the plan live — no reveal-day theatre.
If you already know the use case and just need a partner to build it, skip the consulting engagement and start with a scoped build. We will tell you honestly when that is the right call.
// FAQ
Questions we get a lot.
How is this different from a Big Four AI strategy engagement?
We are engineers first. Our consulting output is opinionated because we have shipped the thing we are recommending — 260+ times. You get an AI roadmap that compiles, not a 90-slide deck with a $2M asterisk.
Do you recommend the tools you sell?
We do not resell anything. Model, cloud, and vendor recommendations are neutral and documented with the trade-offs. If the honest answer is 'buy the off-the-shelf SaaS,' that is what we write.
How long does a typical engagement run?
Discovery and roadmap: 4–6 weeks. Most clients then continue with us into Phase 1 build — usually an 8–12 week pilot — because continuity dramatically reduces handoff loss. You are not obligated to.
Can you work under NDA with sensitive data?
Yes. We default to zero-data-leaves-your-environment for consulting work and sign sector-specific NDAs (BFSI, healthcare, government) before discovery.
What if our data is a mess?
That is the usual starting state and it is the first thing we audit. The roadmap accounts for data remediation as a funded track, not a footnote. Most teams need 4–8 weeks of data work before the first model ships.
// Ready to ship?
Let's talk about what to build first.
Short call. No deck. We will tell you honestly whether we are the right team for your problem.
// Related services
Keep exploring.
AI Agent Development
Agents that actually do the work.
Multi-step agents with real guardrails, evaluation harnesses, and production observability — not demoware.
AI Readiness Assessment
Find out what is actually blocking your first production AI win.
Two-week scored assessment across strategy, data, talent, governance, and infra — with a sequenced plan.